2890. Reshape Data Melt

Description

DataFrame report
+-------------+--------+
| Column Name | Type   |
+-------------+--------+
| product     | object |
| quarter_1   | int    |
| quarter_2   | int    |
| quarter_3   | int    |
| quarter_4   | int    |
+-------------+--------+

Write a solution to reshape the data so that each row represents sales data for a product in a specific quarter.

The result format is in the following example.

Example 1:

Input:
+-------------+-----------+-----------+-----------+-----------+
| product     | quarter_1 | quarter_2 | quarter_3 | quarter_4 |
+-------------+-----------+-----------+-----------+-----------+
| Umbrella    | 417       | 224       | 379       | 611       |
| SleepingBag | 800       | 936       | 93        | 875       |
+-------------+-----------+-----------+-----------+-----------+
Output:
+-------------+-----------+-------+
| product     | quarter   | sales |
+-------------+-----------+-------+
| Umbrella    | quarter_1 | 417   |
| SleepingBag | quarter_1 | 800   |
| Umbrella    | quarter_2 | 224   |
| SleepingBag | quarter_2 | 936   |
| Umbrella    | quarter_3 | 379   |
| SleepingBag | quarter_3 | 93    |
| Umbrella    | quarter_4 | 611   |
| SleepingBag | quarter_4 | 875   |
+-------------+-----------+-------+
Explanation:
The DataFrame is reshaped from wide to long format. Each row represents the sales of a product in a quarter.

Solutions

Solution 1

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import pandas as pd


def meltTable(report: pd.DataFrame) -> pd.DataFrame:
    return pd.melt(report, id_vars=['product'], var_name='quarter', value_name='sales')